lucid.linspace

lucid.linspace(start: int | float | complex, stop: int | float | complex, num: int = 50, /, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor

The linspace function generates a tensor containing a sequence of evenly spaced values between the specified start and stop values. It is particularly useful for creating ranges of values for testing or initialization.

Function Signature

def linspace(
    start: _Scalar,
    stop: _Scalar,
    num: int = 50,
    dtype: type = _base_dtype,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters

  • start (_Scalar): The starting value of the sequence.

  • stop (_Scalar): The ending value of the sequence.

  • num (int, optional): The number of values to generate. Default is 50.

  • dtype (type, optional): The data type of the resulting tensor. Default is _base_dtype.

  • requires_grad (bool, optional): If True, gradients will be tracked for the generated tensor. Default is False.

  • keep_grad (bool, optional): If True, gradients will not be cleared after each backward pass. Default is False.

Returns

  • Tensor: A tensor containing num evenly spaced values between start and stop, inclusive.

Examples

Basic Usage

>>> import lucid
>>> linspace_tensor = lucid.linspace(0, 1, num=5)
>>> print(linspace_tensor)
Tensor([0.0, 0.25, 0.5, 0.75, 1.0], grad=None)

Custom Data Type

>>> linspace_tensor = lucid.linspace(1, 10, num=5, dtype=float)
>>> print(linspace_tensor)
Tensor([ 1.   3.25  5.5   7.75 10.  ], grad=None)

Tracking Gradients

>>> linspace_tensor = lucid.linspace(0, 1, num=5, requires_grad=True)
>>> print(linspace_tensor.requires_grad)
True

Using in a Computation

>>> start = lucid.Tensor(1.0, requires_grad=True)
>>> stop = lucid.Tensor(2.0, requires_grad=True)
>>> linspace_tensor = lucid.linspace(start, stop, num=5)
>>> print(linspace_tensor)
Tensor([1.0, 1.25, 1.5, 1.75, 2.0], grad=None)

Note

  • If num is less than or equal to 1, a single value equal to start is returned.

  • Ensure that start, stop, and num are compatible types to avoid runtime errors.

  • The linspace function is particularly useful for interpolation, initialization, or sampling in various deep learning tasks.